Evolutionary learning, reinforcement learning and fuzzy rules for knowledge acquisition in agent-based systems

Proceedings of the IEEE

Volumn 89, Issue 9, 2001, Pages 1334-1346

  Bonarini, Andrea ^a,b  

^a POLITECNICO DI MILANO   (Italy)

^b APIA (The Italian Association for Artificial Intelligence)   (Italy)

Author keywords

Cooperative systems; Fuzzy systems; Intelligent robots; Learning systems; Mobile robots

Indexed keywords

EID: 0001537801     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/5.949488     Document Type: Article

References (32)

1. M. Asada, H. Kitano, I. Noda, and M. Veloso, "RoboCup today and tomorrow-What we have learned," Ariif. Intel!., vol. 110, no. 2, pp. 193-214, 1999.
2. Sony aibo robot. [Online]. Available: http://www.aibo.com
3. J. J. Grefenstetle and J. Ramsey, "An approach to anytime learning," in Proceedings of the Ninth International Conference on Machine Learning, D. Sleeman and P. Edwards. Eds. San Matco, CA: Morgan Kaufmann, 1992, pp. 189-195.
4. L. A. Zadeh, "Making computers think like people," IEEE Spectr., pp. 26-32, 1984.
5. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1999.
6. [6J W. Spears, K. DeJong, T. Beck, D. Fogel, and H. D. Garis, "An overview of evolutionary computation," in ECML-93, Proceedings European Conference on Machine Learning, P. Brazil, Ed. Berlin, Germany: Springer-Verlag, 1993, pp. 442-459.
7. R. A. Brooks, "A robust layered control system for a mobile robot," IEEEJ. Robot. Automat., vol. RA-2, no. 1, pp. 14-23, 1986.
8. A. Bonarini, "ELF: Learning incomplete fuzzy rule sets for an autonomous robot," in first European Congress on Fuzzy and Intelligent Technologies-EUF1T'93, H.-J. Zimmermann, Ed. Aachen, Germany: Verlag der Augustinus Buchhandlung, 1993, vol. 1, pp. 69-75.
9. [91 A. Bonarini, C. Bonacina, and M. Matteucci, "A framework to support the reinforcement function design in real-world agent-based applications," IEEE Trans. Syst., Man, Cybern. B, 2001, to be published.
10. [ 10] A. Bonarini, "Anytime learning and adaptation of hierarchical fuzzy logic behaviors," Adapt. Behav. J., vol. 5, no. 3-4, pp. 281-315, 1997.
11. A. Bonarini and F. Basso, "Learning to coordinate fuzzy behaviors for autonomous agents," Int. J. Approx. Reason., vol. 17, no. 4, pp. 409-432, 1997.
12. [12J G. J. Klir, B. Yuan, and U. S. Clair, Fuzzy Set Theory: Foundations and Applications. Englewood Cliffs, NJ: Prentice-Hall, 1997.
13. A. Saffiotti, K. Konolige, and E. H. Ruspini, "A multivalued-logic approach to integrating planning and control," Artif. Intell. J., vol. 76, no. 1-2, pp. 481-526, 1995.
14. [141 K. Konolige, K. Myers, E. Ruspini, and A. Saffiotti, "The Saphira architecture: A design for autonomy," / Exper. Theoret. Artif. Intell., vol. 9, no. 1, pp. 215-235, 1997.
15. R. C. Arkin, Behavior-Based Robotics. Cambridge, MA: MIT Press, 1998.
16. A. Bonarini, "Evolutionary learning of fuzzy rules: Competition and cooperation," in Fuzzy Modeling: Paradigms and Practice, W. Pedrycz, Ed. Norwell, MA: Kluwer, 1996, pp. 265-284.
17. D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. London, U.K.: Academic, 1980.
18. P. Stone and M. Veloso, "TPOT-RL: Team-partitioned, opaque-transition reinforcement learning," in RoboCup 98: Robot Soccer World Cup 11, M. Asada, Ed. Berlin. Germany: Springer-Verlag. 1998, pp. 221-236.
19. C. Watkins and P. Dayan, "Q-learning," Mach. Learn., vol. 8, pp. 279-292, 1992.
20. R. S. Sutton, "Learning to predict by the method of temporal differences." Mach. Learn., vol. 3, no. 1, pp. 9-44, 1988.
21. D. Nardi, G. domi, A. Bonarini, A. Chella, G. Clemente, E. Pagello, and M. Piaggio, "Art-azzurra robot team," in RoboCup 99-Robot Soccer World Cup III, H. K. M. Veloso and E. Pagello, Eds. Berlin, German): Springer-Verlag, 2000, pp. 695-698.
22. A. Bonarini, "The body, the mind or the eye, first?," in RoboCup 99 - Robot Soccer World Cup HI, H. K. M. Veloso and E. Pagello, Eds. Berlin. Germany: Springer-Verlag, 2000, pp. 210-219.
23. M. Veloso, H. Kitano, and E. Pagello, RoboCup 99-Robot Soccer World Cup III. Berlin, Germany: Springer-Verlag, 2000, vol. 1856, Lecture Notes in Computer Science and Lecture Notes Artificial Intelligence.
24. A. Bonarini, "Learning fuzzy classifier systems," in Learning Classifier System: New Directions und Concepts, L. Lanzi, W. Stolzmann, and S. W. Wilson, Eds. Berlin, Germany: Springer-Verlag, 2000, pp. 83-106.
25. L. Booker, "Classifier systems that learn internal world models," Mach. Learn., vol. 1, no. 2, pp. 161-192, 1988.
26. S. W, Wilson, "Classifier fitness based on accuracy," Evol. Comput., vol. 3, no. 2, pp. 149-175, 1995.
27. "Classifier systems and the animât problem," Much. Learn., vol. 2, pp. 199-228, 1987.
28. A. Elfes, "Occupancy grids: A stochastic spatial representation for active robot perception," in Prac. 6th Conf. Uncertainty in Al, July 1990, pp. 60-70.
29. S. D. Whitehead and D. H. Ballard, "Learning to perceive and act by trial and error," Mach. Learn., vol. 7, pp. 45-83, 1991.
30. M. J. Mataric, "Reinforcement learning in the multi-robot domain," Auton. Robots, vol. 4, no. 1, pp. 73-83, 1997.
31. "Using communication to reduce locality in multi-robot learning," m Proceedings of the 14ih National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference {AAAI-97/IAAI-97). Menlo Park, CA: AAAI Press, 1997, pp. 643-648.
32. M. Asada, S. Noda, S. Tawaratsumida, and K. Hosoda, "Purposive behavior acquisition on a real robot by a vision based reinforcement learning," in Proc. MLC-COLT '94 Workshop on Robot Learning, S. Mahadevan, Ed. New Brunswick, NJ, 1994.